Low Altitude Aerial Photography Applications for Digital Surface Models Creation in Archaeology

Low Altitude Aerial Photography Applications for DigitalSurface Models Creation in Archaeology

José-Ángel Martínez-del-Pozo, Enrique Cerrillo-Cuenca andErnesto Salas-Tovar

Archaeology Institute of Merida Spanish National Research Council (CSIC)

AbstractLow Altitude Aerial Photography (LAAP) is based on the use of small flying platforms to take photo-graphs from very low altitudes. The acquisition of series of Digital Surface Models (DSMs) and orthoim-ages through photogrammetric and orthorectification processes is one of the main applications. The useof this technique in archaeological sites provides very precise and valuable data, but knowing the reliabil-ity of the method is a key to ensuring that the results are valid, since the repeatability of the method isfundamental to multitemporal studies. The present work shows an analysis of the similarity of DSMsobtained from different stereoscopic pairs from the same area. The reliability of the models has beenassessed by calculating the Standard Deviation (STD) of the altitude values from the models, to obtainmaps displaying the differences among them. Finally, the STD values’ spatial distribution has been studiedto assess if spatial autocorrelation exists by means of the Moran’s Index and Anselin’s Local Moran’sIndex. Results showed good similarity among the models and revealed clearly those areas where themodel must be edited.

1 Introduction

Earth surface representation through Digital Elevations Models (DEMs) is a basic key forstudying the physical environment with computational techniques (Chen and Yue 2010, Fisherand Tate 2006, Smith and Clark 2005). DEMs are numerical representations of the topogra-phy made up of a number of locations with its elevation associated (Chen and Yue 2010,Linder 2009). DEMs store geographical information directly in their structure (values of thevariable, i.e. altitude), and can be used to derive other implicitly stored information insidethem (Felicísimo 1994a), such as slope (first derivative of the surface function) or aspect (direc-tion faced by the surface) maps.

DEM quality and accuracy assessment are fundamental for both DEM producers and finalend users (Aguilar and Mills 2008, Li 1994). From this point of view, Felicísimo (1994b)stresses the need for using a method to evaluate the reliability of the models. Current tech-niques for data collection, processing and rendering have been closely related with the level ofquality of the DEMs (Fisher and Tate 2006). Models are simplified representations of reality,so they are affected by different types of errors caused by data collection inaccuracies and

Address for correspondence: José-Ángel Martínez-del-Pozo, Archaeology Institute of Merida, Spanish National Research Council, Spain.E-mail: [email protected]: The authors would like to thank Pedro Mateos Cruz, director of the Archaeology Institute of Mérida (CSIC), andVíctor Hurtado and Carlos Odriozola, from the Prehistory and Archaeology Department of Seville University, for the data that made thepresent work possible. This article is a contribution to Project PRI09B152: “Análisis urbanístico y territorial de la ciudad romana deContributa Iulia”, funded by Junta de Extremadura, and to Project HAR2011-29907-C3-02: “Técnicas de prospección arqueológica apli-cadas al estudio del poblamiento neolítico en el área centro occidental de la Península Ibérica” funded by the Ministry of Economy andCompetitiveness.

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Research Article Transactions in GIS, 2013, 17(2): 227–246

© 2012 Blackwell Publishing Ltd doi: 10.1111/j.1467-9671.2012.01361.x

blunders during the post-processing (interpolation methods) of the data (Chen and Yue 2010,Fisher and Tate 2006, Weng 2002, Wise 2000). The magnitude of the aforementioned errorsdefines the reliability of the DEMs. Höhle and Höhle (2009) and Li (1994) remark howstudies concerning digital terrain modeling changed from focussing on the DEM creation tech-niques (interpolation methods) mainly to assessing the quality and accuracy of the DEMs(Haneberg 2006). This new way of thinking can be noticed in the rising number of data explo-ration and DEM quality assessment tools implemented in the Geographical Information man-agement software currently available, such as ArcGIS10 or GRASS. Erdogan (2010) and Gonget al. (2000) point out several error sources that affect DEMs: the method for data collection,the post-processing of the data, the interpolation methods used and, finally, the physical char-acteristics of the surface.

The present article deals with the quality assessment of a methodology used to generateDEMs from data captured through Low Altitude Aerial Photography (LAAP). Quilter andAnderson (2000) and Walker and De Vore (1995) define LAAP as the taking of aerial photo-graphs with platforms that do not exceed 300 m of flying height and are able to operate at avery low altitude, i.e. 30 m (Verhoeven 2009, 2011; Baker et al. 2004). Several field surveysover archaeological sites have been carried out using LAAP by generating DEMs with photo-grammetric techniques. The results have been highly accurate DEMs and orthophotographs.

For each survey, an archaeological site was repeatedly photographed from an aerial plat-form (a helium blimp that carries a calibrated semi-professional camera). As the platform posi-tion changes, the same area is photographed from different positions. The parallax and theoverlapping areas among the photos make them suitable to be processed by means of photo-grammetric procedures.

There is a fair amount of literature about DEM quality assessment (see Chen and Yue2010, Erdogan 2010). The main way shown in these papers to quantify the DEM quality isbased on comparing observations and values calculated or predicted by the interpolationalgorithm in the same location (Erdogan 2010). As a result, the Root Mean Square Error(RMSE) is computed. Its value offers an estimation of the general quality of the model (Wise2011, Erdogan 2010). RMSE is the most widely used descriptor to assess DEM error (Fisherand Tate 2006) but, as said before, it is a global measurement of model accuracy that, some-times, is not the best way to estimate the error of data derived from the model, such asslope or aspect maps (Wise 1998).

It is also important to know how widespread the error is on the modeled surface andwhich parts are the most erroneous. Papers by Erdogan (2010), Carlisle (2005), Hunter andGoodchild (1999), Kyriakidis et al. (1999) and Fisher (1998) demonstrate the existence ofpositive spatial autocorrelation in error distribution, as well as its relation with the groundmorphology (Fisher and Tate 2006).

The objective of the present work is to present the results of the application of a LAAPbased methodology to create high resolution DSMs and to assess the precision (repeatability)of this methodology in generating the same results with different stereoscopic pairs. A DSM isdefined as a kind of DEM that represents elevation values of the top of the objects situated onthe terrain, which are the data needed to create orthoimages (Linder 2009). Precision isdefined as the capacity of a method to show the same results from a series of repeated meas-urements taken under the same conditions. The degree of similarity among the DEMs gener-ated for a site will provide information about the precision of the LAAP technique and, hence,its repeatability for other applications such as multitemporal studies (Vericat et al. 2008). Onthe other hand, the DSMs obtained must be post-processed by adding breaklines to adjust themodels where the surface presents abrupt changes (objects, walls, etc.). The final applications

228 J-A Martínez-del-Pozo, E Cerrillo-Cuenca and E Salas-Tovar

© 2012 Blackwell Publishing Ltd Transactions in GIS, 2013, 17(2)

of the DSMs and orthoimages are documenting the archaeological sites. Those DSMs provideaccurate and detailed topographic data of the study areas with sufficient resolution for detec-tion of any topographical abnormalities, whereas the orthoimages can be enhanced to facili-tate photo-interpretation. This kind of study permits identification and location of those areasof the DSMs that must be edited and processed to get the best model of the site.

Combining LAAP and photogrammetry can be classified as a non-destructive analysistechnique and is, therefore, compatible with other routine archaeological methods such as geo-physical or topographical surveys. Moreover, the integration of results into GIS environmentswith high resolution data allows us to compare results from other methodologies.

2 Methodology

2.1 Aerial Platform

The LAAP equipment consists of two basic components: the sensor for data collection and thegeo-positioning system (i.e. a Global Navigation Satellite System [GNSS] station), each ofwhich has its own characteristics, management and components.

Aerial photographs are generated by the use of an unmanned controlled helium airship,similar to that described by Aber et al. (2010) or Jia et al. (2004) which has an attached high-resolution Digital Single-Lens Reflex (DSLR) camera. The airship is a tapered plastic balloon3.90 m long and 1.64 m in diameter at its widest part (Figure 1). The photographic equipmentis coupled to the airship through a structure or gondola, with a weight of 1.66 kg and a radiocontrol system that can adjust both the verticality and the horizontal orientation of the camerawhile shooting from the ground. Thanks to this system, it has been possible to obtain verticalframes (camera axis deviation of less than 5° from the nadir) of the studied sites. Aber et al.(2002) has described a similar system attached to kites, and different equally effective methodsof securing photographic devices to UAVs can be found in literature (e.g. Verhoeven 2009,Baker et al. 2004).

Figure 1 Aerial platform: Unmanned helium blimp with a radio controlled camera attached,119 ¥ 63 mm (300 ¥ 300 DPI)

Low Altitude Aerial Photography Applications 229


The radio control system transmits a video signal to the user along with the image cap-tured by the camera. This real-time transmission facilitates the performance of necessaryadjustments for the optimal positioning of captured frames by controlling the altitude of theblimp and its location on the study area, which is achieved by manually moving the equipmentfrom the ground. Therefore, this part of the data collection process is done selectively andmanually, unlike other methods that program the cameras to take systematic exposures at con-trolled time intervals (Baker et al. 2004).

2.2 The Sensors and their Features

The type and quality of the sensors are the key to the LAAP methodology. Two semi-professional Nikon D90 cameras with AF-S DX Nikkor 18–55 mm f/3.5–5.6G VR lenses wereused in the surveys described in this article. Both cameras are equipped with standard NikonDX sensors. Larger sensors, like the cameras described above, can be accurately calibrated andoffer the appropriate resolution in their final products when used in field surveys (Rieke-Zappet al. 2010).

Photogrammetric procedures require the knowledge of some camera parameters related tointernal geometry (the structure of the lens system) and to lens distortion. As non-metriccameras are being used for photogrammetric surveys, a previous calibration process is neededto know accurately the geometrical parameters of the camera (focal length and principal pointposition) (Remondino and Fraser 2006) and physical parameters related to the distortion(radial and tangential) caused by irregularities on the curved surface of the lens (Sánchez et al.2004, Weng et al. 1992).

For the Nikon D90 calibration, the CameraCalibrator software designed by Photometrixwas used. Following the documentation of the software and the recommendations of Sánchezet al. (2004), the autofocus option of the cameras was deactivated to avoid the automaticmodification of the focal length. The camera was calibrated with the zoom set at a focal lengthof 18 mm in order to obtain the maximum field of view for each photograph. The main reasonfor this decision is that when using a wide-angle lens, the displacements of objects in the stere-oscopic pairing are better represented and the results are therefore more accurate (Linder2009).

Aerial photographs were digitally stored in RAW format because it encodes the informa-tion in 12 bits, registering the photometric and radiometric information of the sensor in awider range than in a processed format. While works like Vericat et al. (2008) use cameras inautomatic mode, for the cases considered here the camera options were manually configured.Camera exposure is usually set at a fixed shutter speed of 1/250, which is sufficient to preventany vibrations or the movement of equipment from affecting the sharpness of the picture, andthe camera is shot when needed. This average speed also sacrifices very little of the depth offield, so the image is always clear enough.

2.3 Sensor Internal Geometry and Lens Distortion Coefficients

Henry et al. (2002) state that the inner orientation is the accurate knowledge of the camerainternal coordinate system in which the data are registered. A calibration procedure for non-metric cameras (Linder 2009) is needed to achieve the true focal length and the principal pointposition (the intersection between the axis of the lens system and the camera sensor). Lens dis-tortion coefficients must also be obtained in order to minimize their influence on the final car-tographic products (Henry et al. 2002, Fryer 1996, Burnside and Marshall 1992).



The importance of the calibration of the lens is evident in Smith et al. (2009), who showedhow the final values of the RMSE in models obtained with calibrated cameras are of a magni-tude order lower than those obtained by means of a postcollection estimation of parametersusing photogrammetric software.

2.4 Solving the External Orientation

The determination of the external orientation is the process that resolves the position andangular orientation of the camera at the moment of the exposure (Erdas 1999) and it is a nec-essary step to be executed prior to the extraction of geographic information.

This procedure determines the real position of the frames in relation to the ground, as wellas their orientation and scale, by relating the image coordinates of some specific references tothe ground coordinates (Ground Control Points, GCPs) of the features referred to a previouslychosen coordinate system. GCPs must appear in both frames of the stereoscopic pair. Thecorrect positioning of the GCPs is one way to maintain the accuracy of the data because inad-equate sampling of points can lead to a poorly-constructed photogrammetric model.

The CGPs positions were measured with a GNSS Trimble R8 GPS receiver station set upin Real-Time Kinematic (RTK) correction mode. The GPS measurements of the CGPs werecorrected with the information provided by a fixed GNSS antenna located on geodetic pointsthat conform to the Spanish Geodetic Network (REGENTE). The accuracy obtained with thismethod in positioning the GCPs was acceptable, as the error was between 1 and 2 cm, whichprovides a very well positioned set of GCPs to address the external orientation.

Aerial photographs were processed in blocks by the Automatic Terrain Extractor toolincluded in the Leica Photogrammetric Suite (LPS-ATE) software package for obtaining DSMand orthophotos. LPS-ATE uses the bundle block adjustment procedure to extract 3D infor-mation from the stereoscopic pairs. The bundle block adjustment technique applies sequen-tially the methods described below (internal orientation and external orientation of the frames)for the triangulation of aerial photographs.

Figure 2 shows the workflow for processing the images to achieve the desired result. Thecoordinates of the GCPs were introduced into a project so that the software can perform anevaluation of GCP quality. The entire block adjustment process is performed on pairs ofoverlapping frames. The LPS-ATE software has algorithms implemented for the shape recog-nition used to locate common points by connecting visible overlapping areas in the photo-graphs (tie points) (Erdas 1999). Tie points and the GCPs are necessary for the aero-triangulation of images because the point cloud that gives rise to the DSM is generated fromthem.

From the set of GCPs and tie points, a quality analysis of the results can be performed bycalculating their RMSE. In case the RMSE is not adequate, both the GCPs and tie points canbe edited (relocated, removed and added) to repeat the triangulation until a proper RMSE isachieved. At this point, we are confident that the resulting model and, hence, the finalorthoimage, will have quality enough. Through the GCPs, a mathematical relationshipbetween the images, the sensor and the land surface is established by LPS-ATE and it allowsthe generation of a DSM from each stereoscopic pair. LPS-ATE can generate as many DSMs asthe number of stereoscopic pairs available and proceed to orthorectificating the photographsacquired with the non-metric camera. All these procedures, from the generation of tie points tothe orthorectification of the frames, are conducted from the LPS-ATE interface and by follow-ing the steps shown in Figure 2.



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2.5 DSM Similarity

The quality of the DSMs obtained from orthorectification is evaluated by the widest-useddescriptor of the DSM error, namely the RMSE value associated with each of them (Fisher andTate 2006). The RMSE values depend directly on the GNSS station accuracy and the geomet-ric disposition of the GCPs over the ground surface. But RMSE is a value that describes theglobal quality of the models (Fisher and Tate 2006), not the possible error-distribution insidethem. The spatial variability of errors inside the model, and its relation with ground character-istics, has been reported by Erdogan (2010) and Fisher (1998), among others.

Taking into account the limitation of RMSE, this article deals with the use of StandardDeviations (STD) as a focal (pixel level) analysis of the dispersion of altitude values among theDSMs in each location or pixel (Hölhe and Höhle 2009, Weng 2002). The models share a sig-nificant overlapping area that can be used to test the method, since it is supposed thatorthorectification procedures generate similar models from different stereoscopic pairs but sup-ported by the same GCPs. It must be stressed that, in the process, raw models have been usedjust as they are obtained from the orthorectification procedure. No editing or post-processing(like breakline addition) was applied to the DSMs to maintain the original outcomes. STD is astatistical measurement of data dispersion, so the less STD in altitude pixel values among themodels, the more similarity exists and, hence, the higher is the precision of the methodology.This means that the methodology is able to calculate very similar models from different stere-oscopic pairs of aerial photographs.

A picture of the spatial STD data distribution is obtained by means of these maps, so themost error-affected areas are localized, to be examined in detail and corrected (Hölhe andHöhle 2009). The STD maps were calculated with the “Cell Statistics” tool provided byArcGIS 10 Spatial Analyst Extension.

2.6 Spatial Analysis of STD Distribution Values

The STD distribution was studied in order to see if some spatial autocorrelation exists betweenthe STD values and the topography of the modeled surface, as suggested by the aforesaidauthors. Both the high spatial resolution of the models and the fact that the local analysisoperates at the pixel level allows the achievement of precise information about the differencesamong the models.

To facilitate the interpretation of the maps, the STD values were classified into five catego-ries as depicted in Table 1. The ranges of each category were proposed by the authors andwere adapted to be more sensitive in the lower values than in the higher, being more detailedregarding the small differences among the models.

Table 1 Intervals of STD values used for the classification of the STD maps

STD values (m) STD Class

0–0.05 STD very low0.05–0.1 STD low0.1–0.2 STD medium0.2–0.3 STD high>0.3 STD very high



The Spatial STD autocorrelation values were analyzed globally and locally. The GlobalMoran’s Index (I) was the algorithm applied to assess the existence of spatial auto-correlationin the STD maps (Erdogan 2010, Zhang and Lin 2007, López Hernández and PalaciosSánchez 2000). This algorithm determines the spatial autocorrelation by considering the vari-able value of one element and those values of the elements that surround it (Griffith 2009). Iis an inferential statistic which is evaluated by the assumption of Complete Spatial Random-ness (CSR) (Esri 2012a, Zhang and Lin 2007) of the variable as a null hypothesis (Ho). Ivalues range from -1 to 1 (Erdogan 2010) and evaluate whether the pattern of the variable isclustered, with positive (0 < I < 1) or negative (-1 < I < 0) spatial autocorrelation, or the vari-able values are dispersed. In the latter case, there is no spatial autocorrelation in the variable,so its distribution is random (I = 0) (Griffith 2009, Zhang and Lin 2007). The performance ofthe test hypothesis comparison is done with p-values and z-score analysis. The p-value can bedefined as the probability of making an type I error (rejecting Ho when it is true) while thez-score is the number of STDs that the I value is deviated from the mean value (Esri 2012a).The p-value determines if the Ho can be assumed (CSR situation) or rejected, so the probabil-ity that the variable follows a random distribution is so low that it can be assumed that thereis a reason for its pattern. The z-score, on the other hand, is taken into account when thep-value is low enough to reject the Ho and it informs about the character of the spatial auto-correlation. Positive z-score values mean that there is a positive spatial autocorrelation andvice versa.

To study the spatial autocorrelation of the STD values, the Cluster and Outlier Analysis(Anselin Local Moran’s Index) (Esri 2012b) were used. This algorithm makes a search forclusters of values that are similar in their magnitude and makes up a map with a representa-tion of the data clusters of high or low values and the outliers, when they exist. The way theclusters are distributed reveals information about the link between the STD values and theground modeled by the DSM. Anselin Local Moran’s Index is fully implemented in ArcGIS 10and it works on vector entities, not on rasters. So the STD maps were sampled with a pointfeatures layer that took the STD values and stored them as an attribute. The point layers weredesigned with the “Create Fishnet” (ArcGIS 10) tool and the point separation was set to20 cm to assure good STD value sampling from the maps. The algorithm classifies the pointsinto four categories: points with high values surrounded by other points with high values(HH); points with low values surrounded by points with low values (LL), points with highvalues surrounded by points with low values (HL) and, finally, points with low values sur-rounded by points with high values (LH) (see ArcGIS tutorial for detailed information). HHand LL are clusters that show spatial correlation of the variable values, while HL and LHpoints are outliers. The presence of outliers points out blunders in the results as the values ofthese points are not related to the ground characteristics of the studied site. By knowing thenumber or the percentage of outliers, the reliability of the DSMs can be assessed (Höhle andHöhle 2009).

3 Studied Sites

LAAP methodology has been evaluated over the data acquired in three different archaeologicalsites (Figure 3). The areas have been chosen according to their heterogeneity in order to assessthe results under different topographical conditions. So far we have recorded nearly a dozenarchaeological sites by LAAP and photogrammetry. The study of these is included in various



exploration projects developed regionally, covering the interval from the Neolithic to theRoman period.

3.1 Los Cercos Archaeological Site

Los Cercos (Figure 3) is a small Roman town (Mateos et al. 2009) located near Medina de LasTorres (in the south of Badajoz province) on a slight rise of ground. Excavations undertakenover the past two years have focused on documenting the wall next to one of the gates of thecity and, later, part of the forum complex which is located at the top of the hill. The applica-tion of LAAP was performed with the aim of documenting the different phases in which theexcavation was done and identifying a number of architectural elements in the area that hadnot yet been excavated. The use of DSM and digital elevation models (DEMs) in this case isnot as important as generating an orthoimage to allow the correct planimetric documentationof structures during excavation.

3.2 Veguillas-1 Archaeological Site

Veguillas 1 is located in north Cáceres province, near the village of Jarandilla de La Vera.On this occasion, our team was developing a survey project focused on the analysis ofMesolithic and Neolithic sites. Here LAAP was used to document several small sites sur-rounding Veguillas 1, as well as this small fortified site, which can be dated to the Thirdmillennium BC and belongs to a local Bromze Age settlement. As no excavation has beendeveloped on the site, we used photogrammetry to map the walls of the precinct and theextension of the site. DSM was as important as the orthoimage because both products canbe used jointly to accurately delimit the site.

Figure 3 Archaeological sites where the LAAP methodology was tested



3.3 La Pijotilla Archaeological Site

La Pijotilla site has been excavated over the last 30 years by Professor V. Hurtado, from SevilleUniversity. La Pijotilla is located near the city of Badajoz. The site belongs to the Bronze Age,Third millennium BC, and was one of the largest concentrations of population during thatperiod in the Iberian Peninsula, with approximately 80 ha of occupied site (Hurtado Pérez1995, Hurtado et al. 2000). The field season of 2011 focused on the excavation of some hutslocated at the central part of the site. The documentation, DSM and orthoimage, were used tomake the plan of the site, which needed a more reduced scale than that of the Los Cercos sites.

4 Results and Discussion

4.1 DSMs and Orthoimages

4.1.1 Los Cercos

Figure 4 shows the results obtained from LAAP fieldwork. The images that we used in thisstudy were taken in February 2010 in sunny conditions with little wind and from an approxi-mate height of 70 m. The DSM derived from the photographs was generated with a spatialresolution of 0.05 m, which allows for a very detailed model of the excavated area at the timeof taking the photographs. The high resolution of the model allows us to distinguish the ele-ments that have altered the topography of the site (e.g. land excavation) and also the lessprominent objects. The model allows an accurate distinction of the limits of the excavationand the walls that make up the structures of the forum in the Roman city (Mateos et al. 2009)(Figure 4a).

Orthophotos derived from the model have a spatial resolution of 0.02 m (Figure 4b). Inthe orthophotography, we can clearly see the structures that have been already documentedand their resemblance with the surface represented by the model.

Figure 4 LAAP results for Los Cercos archaeological site: (a) DSM obtained for the site; and (b)Orthoimage



4.1.2 Veguillas 1

This site was photographed in October 2010 with optimal weather conditions, on a sunny daywith little wind, allowing placement of the sensor zenith over the area of interest. The heightof the platform in this case was about 100 m. A DSM was obtained by photogrammetric res-titution, orthophotographs were elaborated to study and interpret the collapsing walls of thesmall fortification: the uneven distribution of vegetation, depending on the shape of collapsedwalls, allowed us to draw the extension of a walled double fortification on the orthoimagewith the DSM support. The DSM was generated with a resolution of 0.2 m/pixel and permitsclear highlighting of the perimeter of the outside path of the wall, which has a slightly ellipsoi-dal form (Figure 5) with about a 50 m semi-major axis and between 30 and 40 m semi-minoraxis.

The grainy appearance of the DSM is due to the vegetation that covers the site; this effectis evident when comparing the DSM (Figure 5a) with its associated orthoimage (Figure 5b),whose spatial resolution (0.03 m) allows us to distinguish among areas of vegetation, trees,and isolated bushes. In this case, vegetation plays a relevant role: on the one hand it providesinformation about the archaeological site because anomalies related to the two walled ringsexhibited by this small fortification can be identified in its distribution pattern (Figure 5b). Onthe other hand, it becomes a factor of error when stereoscopic information is extracted fromthe pairs, due to the heterogeneous height of the plants and their susceptibility to being movedby wind, which caused problems when ties between the photographs were established. TheRMSE is similar to previous restitutions and is weighted at 0.14 pixels in the entire image. Inthis case, the operational time for the flight and subsequent data processing was less than thetime that any other type of archaeological documentation technique would require, withacceptable errors for this type of work.

4.1.3 La Pijotilla

The La Pijotilla site was photographed on a windy day at the end of July 2011 and the plat-form was elevated 15 m. We found some problems in placing the sensor over the site, so a

Figure 5 LAAP results for Veguillas-1 archaeological site: (a) DSM obtained for the site; (b)Orthoimage



great number of pictures was taken. The orthoimage clearly shows superimposed wallsbelonging to different domestic structures (probably huts) belonging to different phases ofthe site.

Very high quality results were obtained for the La Pijotilla archaeological site. Figure 6shows the DSM and the orthophotograph obtained from the orthorectification process. TheDSM (Figure 6a) has a final resolution of 0.02 m and the orthoimage 0.005 m. The high reso-lution of the products obtained for this site is remarkable; it allows the study of uncoveredstructures and the identification of very small details. This reason endorses the validity of theLAAP methodology for the study of very small areas from very low flight altitudes.

4.2 DSM Comparison Analysis

4.2.1 STD distribution maps

The Los Cercos archaeological site (Figure 4) is a very flat area partly excavated at themoment of data collection. The site is located on a hill and shows a very flat surface due to thegeomorphology of the site and to flattening work to prepare the area for agriculture.

Six DSMs were obtained for Los Cercos from the aerial survey. The STD values of themap range from 0.0042 to 0.5083, showing up the areas where the dispersion of values wasemphasized (Figure 7a). The whole map presents low and medium values of STD and thehighest values of STD are located in areas where the DSM surface breaks abruptly (Figure 7b).In the figure we can see the correspondence between the highest values of STD and the wallsor trenches where the STD values rise up to 0.5 m. The statistics for each STD class can berepresented by a histogram (Figure 8). The majority of the pixels are classified as Very LowSTD and Low STD (57.5%) while Medium STD (37.9%) also has some relevance (Table 2).Most of the pixels (95.4%) are in the aforementioned categories. So it can be concluded thatthe method gave reliable results for the Los Cercos archaeological site. In accordance withCarlisle (2005), the highest values of STD define the abrupt changes on the ground, as can be

Figure 6 LAAP results for La Pijotilla archaeological site: (a) DSM obtained for the site; (b)Orthoimage



seen in the sides of the walls, the perimeter of the parcel and the trench in the SW of the exca-vation (Figure 7b).

The survey of Veguillas-1 showed some difficulties because it is partly covered by trees andvegetation (Figure 9). A group of four DSMs of 0.1 m of spatial resolution were chosen forcomparison. The final STD map obtained from the four models presents a range of STD valuesfrom 0 to 1.1508 m (Figure 9a). The wider range in this case is caused by the presence of highvegetation that makes it difficult to recognize the tie points by means of the LPS algorithms.The highest values of STD (Figure 9a) are located over the areas covered by vegetation, beinghigher where the vegetation is more dense (Figure 9b). When classifying the STD map(Figure 9b) the vegetation areas are clearly classified as Very High STD values, whereas theVery Low values are related to bare areas of the site of study. Analyzing the histogram of theSTD reclassification map (Figure 10), 31.7% of the surface has been classified as Very Low

Figure 7 DSM comparison for the Los Cercos archaeological site: (a) STD map; and (b) ClassifiedSTD map

Figure 8 Histogram of the surface percentage of each STD class for the Los Cercos archaeologicalsite



(10.2%) or Low (21.5%) STD whereas the Medium values of STD comprise 37.3% of thesurface (Table 3). There is a close similarity among the models in those uncovered areas butthe vegetation triggers the diffusion in the altitude values for each pixel.

The excavated area of the last archaeological site, La Pijotilla, has more reduced dimen-sions than the previous sites. La Pijotilla is located on a very flat surface in a cropped areawhere agricultural activities have smoothed the ground. When surveyed, the site was partlyexcavated, revealing a series of buried structures (Figure 11).

As in the case of Veguillas-1, four DSMs were used for the STD map calculation. The STDvalues range from 0 to 0.28 m (Figure 11a), which is narrower than those obtained for theother sites. In Figure 11b it can be seen how the lowest errors are located inside the excavatedareas that are surrounded by the GCP used in the orthorectification. As in the previous cases,the highest values of STD are in the margins of the DSMs, located over the structures thathave been disclosed and in the borders of the archaeological excavations.

The results for La Pijotilla are the best compared with Los Cercos and Veguillas-1. Of theSTD values, 91.5% are classified as Very Low (67.8%) and Low (23.7) (Table 4). The otherclasses are almost absent. Only STD Medium has some representation, with a 8.3% of thepixels (Figure 12).

Table 2 Statistics for the classified STD map of the Los Cercos archaeological site

STD Class Pixels count Surface (m2) % Surface

STD very low 66,127 661.27 19.08STD low 133,121 1,331.21 38.42STD medium 131,369 1,313.69 37.91STD high 12,641 126.41 3.65STD very high 3,230 32.3 0.93

Figure 9 DSM comparison for the Veguillas-1 archaeological site: (a) STD map; and (b) ClassifiedSTD map



4.2.2 Spatial autocorrelation and outliers analysis

Once the reliability of the method to generate DSMs from LAAP data had been assessed, thenext step was to confirm the existence of spatial autocorrelation in the values of STD andwhether there was a relation with the topography or the characteristics of the ground.

The results of the application of the Global Moran’s Index revealed that, in the threecases, the STD values are spatially correlated because the Moran’s Index values are signifi-cantly different from 0 (Table 5). The significant test establishes H0 as the randomness of theSTD distribution, so I = 0, but in all the cases the p-values are 0, so H0 can be rejected and itcan be assumed that the STD value distributions are not caused by randomness. As the spatialautocorrelation can be positive or negative, the z-score parameter is used to evaluate how vari-able the behavior is. The z-score values calculated by the algorithm are very high and positive(Table 5), which means a very positive spatial autocorrelation of the STD values.

Finally, to assure the degree of autocorrelation of the STD values, the Cluster and OutlierAnalysis (Anselin Local Moran’s Index) was applied to the study areas. The Anselin Index triesto group the values into clusters and detects outliers in the STD values, if they exist, by locallyanalyzing the STD values between a point and its neighbors. In three cases there is almost anabsence of outliers (Table 6) while there are evident clusters of high and low values of STD(Figure 13). As outliers are related to erroneous data caused by random phenomena, theabsence of outliers allows us to say that the high and low STD values are due to factors likethe topography of the terrain, as can be seen in Los Cercos and La Pijotilla (Figures 13a and c)

Figure 10 Histogram of the surface percentage of each STD class for the Veguillas-1 archaeologicalsite

Table 3 Veguillas-1: Statistics for the classified STD map for Veguillas-1 archaeological site

STD Class Pixels count Surface (m2) % Surface

STD very low 14,360 574.4 10.19STD low 30,311 1,212.44 21.50STD medium 52,544 2,101.76 37.27STD high 23,481 939.24 16.65STD very high 20,293 811.72 14.39



where the walls and the excavations are highlighted by the clusters, or due to problems duringthe data collection. For example, the HH clusters are located where vegetation is present whilethe LL clusters are related to areas without vegetation in Veguillas-1 (Figure 13b).

5 Conclusions

Despite the introduction of laser sensors in Archaeology, many archaeological teams haveopted to use conventional photogrammetric documentation. Without going into much detail,the advantages of this technique can be stated as follows: reduced costs of the work system,possibility of working on a small scale and, especially, the possibility of processing old aerialphotographs and incorporating them as additional documents for analysis.

The method has proven to be easily applied in the study of archaeological sites or areasthat require a prior assessment of their archaeological qualities. The nature of the informationobtained from the LAAP ranges from simple data to the documentation and preliminary studyof archaeological sites, such as Veguillas-1, or even data designed to monitor the excavationworks, as at the Los Cercos site, where information from both digitalizing and preliminary

Figure 11 DSM comparison for the La Pijotilla archaeological site: (a) STD map; and (b) ClassifiedSTD map

Table 4 Statistics for the classified STD map of the La Pijotilla archaeological site

STD Classs Pixels count Surface (m2) % Surface

STD very low 306,997 122.80 67.79STD low 107,139 42.86 23.66STD medium 37,571 15.03 8.30STD high 1,175 0.47 0.26STD very high 0 0.00 0.00



interpretation of the structures can be extracted to plan the excavation strategy and make deci-sions about its development. The simplicity of operating and transportating the equipmentallows repeating campaigns on the same site, thus providing a multi-temporal record tomanage excavation development based on the results.

The problems of this method when collecting data are determined by the meteorologicalconditions, because it is not possible to fly with wind speeds over 10 km/h. Moreover, the loca-tion of the study is also essential. The size of the platform requires that archaeological sites areaccessible and located in obstacle-free open areas.

In all cases, the method provides very high quality and high spatial resolution Geographi-cal Information, especially considering the extent of the areas studied and flight altitudes of theaerial platform used.

Table 5 Moran’s Index values obtained at each archaeological site and its associated p-values andz-score

ArchaeologicalSite

Moran’sIndex p-Value z-Score

Horejection Spatial autocorrelation

Cercos 0.9689 0.0000 402.2378 YES PositiveSAVeguillas-1 0.9626 0.0000 509.8547 YES PositiveSALa Pijotilla 0.8651 0.0000 81.6842 YES PositiveSA

Figure 12 Histogram of the surface percentage of each STD class for the La Pijotilla archaeologicalsite

Table 6 Results for the Cluster and Outlier Analysis (Anselin Local Moran’s Index)

Archaeological Site HL values LH values Total points number % outliers

Los Cercos 0 1 86,516 0.001Veguillas-1 0 0 140,703 0La Pijotilla 0 0 4,531 0



The generated DSMs provide accurate and detailed topographic data of the studied areasand sufficient resolution for detection of any topographical abnormalities, whereas theorthoimages can be enhanced to facilitate photo-interpretation. A favorable sign is that RMSEvalues calculated for the aero-triangulation by LPS-ATE were very low in every case. Thus, thetotal values for the models were below a RMSE value of 0.14 pixels: the RMSE of the hori-zontal components (X and Y) varied between 2 and 3 cm, while the RMSE associated with thevertical component models (Z) reached sub-centimeter RMSE.

A study of the precision of the method was carried out by comparing the DSMs obtainedfrom different stereoscopic pairs. The stereoscopic pairs used for each site were taken in thesame survey under very similar conditions, which allows for comparison of the DSMs to assessif the method is able to generate the same data. The STD of the altitude value of the DSMswas used as the indicator of the method’s precision. In general, the STD maps show very goodresults over the interest areas, where the STD values are mostly classified as Low and VeryLow. In all the cases, the DSMs are very similar in the central parts of the sites, where theinterest area is located and always inside the perimeter defined by the CGPs disposition. Theworst results are outside the CGP perimeter and affect the margins of the model, which are notvaluable for the investigations. The correct disposition of the GCP is of vital importance, asthey define the inner area where the models are more similar. The analysis of the STD valuedistributions relied on Moran’s Index and Anselin Local Moran’s Index, which reveal how theSTD values are spatially autocorrelated and associated with abrupt changes in the groundsurface (topography, walls, etc.). The highest errors in comparing the models occurred inVeguillas-1, where areas covered by vegetation show the greatest diffusion in the STD values.

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